IntegrationTest_txt_gensim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\gensim\\utils.py:855: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n"
     ]
    }
   ],
   "source": [
    "from dariah_topics import preprocessing as pre\n",
    "from dariah_topics import visualization as visual\n",
    "from dariah_topics import evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Liste mit Dateinamen erzeugen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:92: DeprecationWarning: DisplayFormatter._ipython_display_formatter_default is deprecated: use @default decorator instead.\n",
      "  def _ipython_display_formatter_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:98: DeprecationWarning: DisplayFormatter._formatters_default is deprecated: use @default decorator instead.\n",
      "  def _formatters_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "c:\\users\\philip\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\IPython\\core\\formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['corpus_txt\\\\Doyle_AScandalinBohemia.txt',\n",
       " 'corpus_txt\\\\Doyle_AStudyinScarlet.txt',\n",
       " 'corpus_txt\\\\Doyle_TheHoundoftheBaskervilles.txt',\n",
       " 'corpus_txt\\\\Doyle_TheSignoftheFour.txt',\n",
       " 'corpus_txt\\\\Howard_GodsoftheNorth.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_txt = \"corpus_txt\"\n",
    "#path_txt = \"grenzbote_plain/*/\"\n",
    "#path_txt = \"wiki/\"\n",
    "\n",
    "doclist_txt = pre.create_document_list(path_txt)\n",
    "assert doclist_txt, \"No documents found\"\n",
    "doclist_txt[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Liste mit Dokumentenlabels erzeugen - (Funktion wird durch Thorsten's generischere Funktion ersetzt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Doyle_AScandalinBohemia',\n",
       " 'Doyle_AStudyinScarlet',\n",
       " 'Doyle_TheHoundoftheBaskervilles',\n",
       " 'Doyle_TheSignoftheFour',\n",
       " 'Howard_GodsoftheNorth']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_labels = list(pre.get_labels(doclist_txt))\n",
    "doc_labels[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corpus laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "corpus_txt = pre.read_from_txt(doclist_txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "doc_tokens = [list(pre.tokenize(txt)) for txt in list(corpus_txt)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "id_types, doc_ids = pre.create_dictionaries(doc_labels, doc_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sparse BOW Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sparse_bow = pre.create_mm(doc_labels, doc_tokens, id_types, doc_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <th>token_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>16384</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16388</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8197</th>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4103</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8203</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "doc_id token_id     \n",
       "1      16384       1\n",
       "       16388       1\n",
       "       8197      244\n",
       "       4103        1\n",
       "       8203        1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_bow[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modeling with Gensim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving Sparse BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre.save_bow_mm(sparse_bow, \"gensim_txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Market Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import MmCorpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mm = MmCorpus(\"gensim_txt.mm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "doc2id = {value : key for key, value in doc_ids.items()}\n",
    "type2id = {value : key for key, value in id_types.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Remove Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "type2id = {value : key for key, value in id_types.items()}\n",
    "sparse_bow_collapsed = sparse_bow.groupby(sparse_bow.index.get_level_values('token_id')).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sparse_bow_hapax = sparse_bow_collapsed.loc[sparse_bow_collapsed[0] == 1]\n",
    "hapax = [type2id[key] for key in sparse_bow_hapax.index.get_level_values('token_id')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "len(hapax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "basepath = os.path.abspath('.')\n",
    "\n",
    "with open(os.path.join(basepath, \"tutorial_supplementals\", \"stopwords\", \"de.txt\"), 'r', encoding = 'utf-8') as f: \n",
    "    stopword_list = f.read().split('\\n')\n",
    "    \n",
    "stopword_list = set(stopword_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hapax_from_remove = pre.find_hapax(sparse_bow, id_types)\n",
    "stopwords_from_remove = pre.find_stopwords(sparse_bow, id_types, mfw=75)\n",
    "\n",
    "#features_to_be_removed = set(hapax_from_remove + stopwords_from_remove)\n",
    "features_to_be_removed = stopwords_from_remove\n",
    "\n",
    "sparse_bow_short = pre.remove_features(sparse_bow, id_types, features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre.save_bow_mm(sparse_bow_short, \"gensim_txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mm = MmCorpus(\"gensim_txt.mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert sparse_bow to list of (doc, tokens) tuples (like doc2bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "doc2bow_list = []\n",
    "\n",
    "for doc in sparse_bow_short.index.groupby(sparse_bow_short.index.get_level_values('doc_id')):\n",
    "    temp = [(token, count) for token, count in zip(sparse_bow_short.loc[doc].index, sparse_bow_short.loc[doc][0])]\n",
    "    doc2bow_list.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc2bow_list = pre.make_doc2bow_list(sparse_bow_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(doc2bow_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#model = LdaModel(corpus=mm, id2word=type2id, num_topics=60, alpha = \"symmetric\", passes = 10) #import momentan in visual \n",
    "# -> da ich mir noch nicht sicher bin, welche Funktionen in das tm_gensim.py sollen\n",
    "model = LdaModel(corpus=mm, id2word=type2id, num_topics=20, passes = 10, iterations = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.13228416239327814),\n",
       " (5, 0.18926606754891051),\n",
       " (6, 0.32090231624688376),\n",
       " (15, 0.13312014179482362),\n",
       " (17, 0.18113449370015158),\n",
       " (18, 0.016185686387508974),\n",
       " (19, 0.013475572672369885)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_document_topics(doc2bow_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['smudge',\n",
       " 'seasoned',\n",
       " 'boulder-sprinkled',\n",
       " 'refreshing',\n",
       " 'vindictiveness',\n",
       " 'compounded',\n",
       " 'lil',\n",
       " 'mauvais',\n",
       " 'destroyer',\n",
       " 'unimaginable']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Anzeige der keywords für topic n\n",
    "n = 1\n",
    "topic_nr_x = model.get_topic_terms(n)\n",
    "\n",
    "topicTerms = [type2id[i[0]] for i in topic_nr_x]\n",
    "topicTerms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.000*\"vegetablelike\" + 0.000*\"logic\" + 0.000*\"strikes\" + 0.000*\"two-dimensional\" + 0.000*\"ransom\" + 0.000*\"pigmentation\" + 0.000*\"squelching\" + 0.000*\"stockwell\" + 0.000*\"dispirited\" + 0.000*\"oldest\"'),\n",
       " (1,\n",
       "  '0.007*\"smudge\" + 0.005*\"seasoned\" + 0.005*\"boulder-sprinkled\" + 0.004*\"refreshing\" + 0.004*\"vindictiveness\" + 0.004*\"compounded\" + 0.004*\"lil\" + 0.004*\"mauvais\" + 0.004*\"destroyer\" + 0.004*\"unimaginable\"'),\n",
       " (2,\n",
       "  '0.000*\"vegetablelike\" + 0.000*\"self-possession\" + 0.000*\"wednesday\" + 0.000*\"two-dimensional\" + 0.000*\"stockwell\" + 0.000*\"unimaginable\" + 0.000*\"cool\" + 0.000*\"refreshing\" + 0.000*\"pigmentation\" + 0.000*\"domes\"'),\n",
       " (3,\n",
       "  '0.000*\"depravity\" + 0.000*\"shiny-strong\" + 0.000*\"logic\" + 0.000*\"outlined\" + 0.000*\"ransom\" + 0.000*\"squelching\" + 0.000*\"police-sergeant\" + 0.000*\"pigmentation\" + 0.000*\"haunch\" + 0.000*\"oldest\"'),\n",
       " (4,\n",
       "  '0.000*\"strikes\" + 0.000*\"haunch\" + 0.000*\"sculptureless\" + 0.000*\"ransom\" + 0.000*\"lil\" + 0.000*\"vegetablelike\" + 0.000*\"honeycombings\" + 0.000*\"oldest\" + 0.000*\"refreshing\" + 0.000*\"police-sergeant\"'),\n",
       " (5,\n",
       "  '0.004*\"stockwell\" + 0.004*\"police-sergeant\" + 0.003*\"vegetablelike\" + 0.003*\"understand!i\" + 0.003*\"oldest\" + 0.003*\"haunch\" + 0.003*\"domes\" + 0.003*\"plumstead\" + 0.003*\"swears\" + 0.003*\"cyclopean\"'),\n",
       " (6,\n",
       "  '0.006*\"depravity\" + 0.004*\"haunch\" + 0.004*\"two-dimensional\" + 0.004*\"poltoos\" + 0.004*\"squelching\" + 0.004*\"shred\" + 0.004*\"ransom\" + 0.004*\"vegetablelike\" + 0.004*\"self-possession\" + 0.004*\"outlined\"'),\n",
       " (7,\n",
       "  '0.001*\"heel-rope\" + 0.001*\"pigmentation\" + 0.001*\"refreshing\" + 0.001*\"candelabrum\" + 0.001*\"logic\" + 0.001*\"lil\" + 0.001*\"sculptureless\" + 0.001*\"metal-colored\" + 0.001*\"unimaginable\" + 0.001*\"noisy\"'),\n",
       " (8,\n",
       "  '0.000*\"pigmentation\" + 0.000*\"domes\" + 0.000*\"logic\" + 0.000*\"haunch\" + 0.000*\"lil\" + 0.000*\"squelching\" + 0.000*\"self-possession\" + 0.000*\"police-sergeant\" + 0.000*\"ransom\" + 0.000*\"oldest\"'),\n",
       " (9,\n",
       "  '0.000*\"honeycombings\" + 0.000*\"self-possession\" + 0.000*\"lil\" + 0.000*\"oldest\" + 0.000*\"police-sergeant\" + 0.000*\"squelching\" + 0.000*\"stockwell\" + 0.000*\"strikes\" + 0.000*\"vegetablelike\" + 0.000*\"sculptureless\"'),\n",
       " (10,\n",
       "  '0.008*\"moth\" + 0.007*\"gravitating\" + 0.005*\"domes\" + 0.005*\"unastronomical\" + 0.004*\"attempts\" + 0.004*\"drawling\" + 0.004*\"powerfully\" + 0.004*\"police-sergeant\" + 0.004*\"bows\" + 0.003*\"salted\"'),\n",
       " (11,\n",
       "  '0.000*\"self-possession\" + 0.000*\"two-dimensional\" + 0.000*\"outlined\" + 0.000*\"shred\" + 0.000*\"haunch\" + 0.000*\"vegetablelike\" + 0.000*\"stockwell\" + 0.000*\"squelching\" + 0.000*\"mine-sweepers\" + 0.000*\"poltoos\"'),\n",
       " (12,\n",
       "  '0.000*\"pigmentation\" + 0.000*\"depravity\" + 0.000*\"self-possession\" + 0.000*\"shred\" + 0.000*\"vegetablelike\" + 0.000*\"strikes\" + 0.000*\"outlined\" + 0.000*\"candelabrum\" + 0.000*\"two-dimensional\" + 0.000*\"stockwell\"'),\n",
       " (13,\n",
       "  '0.007*\"juncture\" + 0.006*\"disarranged\" + 0.006*\"stretch\" + 0.004*\"two-dimensional\" + 0.004*\"outfits\" + 0.003*\"hideously\" + 0.003*\"mine-sweepers\" + 0.003*\"nothwendig\" + 0.003*\"blasphemously\" + 0.003*\"deaths\"'),\n",
       " (14,\n",
       "  '0.000*\"self-possession\" + 0.000*\"sculptureless\" + 0.000*\"two-dimensional\" + 0.000*\"stockwell\" + 0.000*\"oldest\" + 0.000*\"logic\" + 0.000*\"outlined\" + 0.000*\"vegetablelike\" + 0.000*\"dispirited\" + 0.000*\"haunch\"'),\n",
       " (15,\n",
       "  '0.012*\"pendulous\" + 0.008*\"snarled\" + 0.008*\"films\" + 0.007*\"logic\" + 0.006*\"decade\" + 0.006*\"remarkable-looking\" + 0.006*\"mars\" + 0.006*\"pigmentation\" + 0.006*\"ancestors\" + 0.005*\"deposit\"'),\n",
       " (16,\n",
       "  '0.001*\"lush\" + 0.001*\"flyers\" + 0.001*\"brook\" + 0.001*\"consequences\" + 0.001*\"official-looking\" + 0.001*\"corroborative\" + 0.001*\"russian\" + 0.001*\"panted\" + 0.001*\"wails\" + 0.001*\"broadcloth\"'),\n",
       " (17,\n",
       "  '0.006*\"indulged\" + 0.004*\"domes\" + 0.004*\"two-dimensional\" + 0.004*\"honeycombings\" + 0.003*\"pigmentation\" + 0.003*\"weighty\" + 0.003*\"guardedly\" + 0.003*\"self-possession\" + 0.003*\"powerfully\" + 0.003*\"refreshing\"'),\n",
       " (18,\n",
       "  '0.009*\"haunch\" + 0.008*\"exclamation\" + 0.007*\"billow\" + 0.006*\"dried\" + 0.005*\"glimpse\" + 0.005*\"jumpy\" + 0.004*\"dispirited\" + 0.004*\"gladness\" + 0.004*\"petra\" + 0.004*\"flounce\"'),\n",
       " (19,\n",
       "  '0.007*\"half-carrying\" + 0.007*\"cloudily\" + 0.006*\"doomed\" + 0.004*\"screws\" + 0.004*\"two-dimensional\" + 0.004*\"strikes\" + 0.003*\"nasal\" + 0.003*\"pigmentation\" + 0.003*\"day\\'s\" + 0.003*\"feverish\"')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Topic probabilities\n",
    "topics = model.show_topics(num_topics = 40)\n",
    "topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "topics_df = pre.gensim2dataframe(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>smudge</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>depravity</td>\n",
       "      <td>strikes</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>depravity</td>\n",
       "      <td>heel-rope</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>honeycombings</td>\n",
       "      <td>moth</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>juncture</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>pendulous</td>\n",
       "      <td>lush</td>\n",
       "      <td>indulged</td>\n",
       "      <td>haunch</td>\n",
       "      <td>half-carrying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>logic</td>\n",
       "      <td>seasoned</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>shiny-strong</td>\n",
       "      <td>haunch</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>haunch</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>domes</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>gravitating</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>depravity</td>\n",
       "      <td>disarranged</td>\n",
       "      <td>sculptureless</td>\n",
       "      <td>snarled</td>\n",
       "      <td>flyers</td>\n",
       "      <td>domes</td>\n",
       "      <td>exclamation</td>\n",
       "      <td>cloudily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>strikes</td>\n",
       "      <td>boulder-sprinkled</td>\n",
       "      <td>wednesday</td>\n",
       "      <td>logic</td>\n",
       "      <td>sculptureless</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>logic</td>\n",
       "      <td>lil</td>\n",
       "      <td>domes</td>\n",
       "      <td>outlined</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>stretch</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>films</td>\n",
       "      <td>brook</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>billow</td>\n",
       "      <td>doomed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>outlined</td>\n",
       "      <td>ransom</td>\n",
       "      <td>understand!i</td>\n",
       "      <td>poltoos</td>\n",
       "      <td>candelabrum</td>\n",
       "      <td>haunch</td>\n",
       "      <td>oldest</td>\n",
       "      <td>unastronomical</td>\n",
       "      <td>shred</td>\n",
       "      <td>shred</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>logic</td>\n",
       "      <td>consequences</td>\n",
       "      <td>honeycombings</td>\n",
       "      <td>dried</td>\n",
       "      <td>screws</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ransom</td>\n",
       "      <td>vindictiveness</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>ransom</td>\n",
       "      <td>lil</td>\n",
       "      <td>oldest</td>\n",
       "      <td>squelching</td>\n",
       "      <td>logic</td>\n",
       "      <td>lil</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>attempts</td>\n",
       "      <td>haunch</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>outfits</td>\n",
       "      <td>oldest</td>\n",
       "      <td>decade</td>\n",
       "      <td>official-looking</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>glimpse</td>\n",
       "      <td>two-dimensional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pigmentation</td>\n",
       "      <td>compounded</td>\n",
       "      <td>unimaginable</td>\n",
       "      <td>squelching</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>haunch</td>\n",
       "      <td>shred</td>\n",
       "      <td>lil</td>\n",
       "      <td>squelching</td>\n",
       "      <td>squelching</td>\n",
       "      <td>drawling</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>strikes</td>\n",
       "      <td>hideously</td>\n",
       "      <td>logic</td>\n",
       "      <td>remarkable-looking</td>\n",
       "      <td>corroborative</td>\n",
       "      <td>weighty</td>\n",
       "      <td>jumpy</td>\n",
       "      <td>strikes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>squelching</td>\n",
       "      <td>lil</td>\n",
       "      <td>cool</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>honeycombings</td>\n",
       "      <td>domes</td>\n",
       "      <td>ransom</td>\n",
       "      <td>sculptureless</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>powerfully</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>outlined</td>\n",
       "      <td>mine-sweepers</td>\n",
       "      <td>outlined</td>\n",
       "      <td>mars</td>\n",
       "      <td>russian</td>\n",
       "      <td>guardedly</td>\n",
       "      <td>dispirited</td>\n",
       "      <td>nasal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>stockwell</td>\n",
       "      <td>mauvais</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>oldest</td>\n",
       "      <td>plumstead</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>metal-colored</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>strikes</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>squelching</td>\n",
       "      <td>candelabrum</td>\n",
       "      <td>nothwendig</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>panted</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>gladness</td>\n",
       "      <td>pigmentation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dispirited</td>\n",
       "      <td>destroyer</td>\n",
       "      <td>pigmentation</td>\n",
       "      <td>haunch</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>swears</td>\n",
       "      <td>self-possession</td>\n",
       "      <td>unimaginable</td>\n",
       "      <td>ransom</td>\n",
       "      <td>vegetablelike</td>\n",
       "      <td>bows</td>\n",
       "      <td>mine-sweepers</td>\n",
       "      <td>two-dimensional</td>\n",
       "      <td>blasphemously</td>\n",
       "      <td>dispirited</td>\n",
       "      <td>ancestors</td>\n",
       "      <td>wails</td>\n",
       "      <td>powerfully</td>\n",
       "      <td>petra</td>\n",
       "      <td>day's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>oldest</td>\n",
       "      <td>unimaginable</td>\n",
       "      <td>domes</td>\n",
       "      <td>oldest</td>\n",
       "      <td>police-sergeant</td>\n",
       "      <td>cyclopean</td>\n",
       "      <td>outlined</td>\n",
       "      <td>noisy</td>\n",
       "      <td>oldest</td>\n",
       "      <td>sculptureless</td>\n",
       "      <td>salted</td>\n",
       "      <td>poltoos</td>\n",
       "      <td>stockwell</td>\n",
       "      <td>deaths</td>\n",
       "      <td>haunch</td>\n",
       "      <td>deposit</td>\n",
       "      <td>broadcloth</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>flounce</td>\n",
       "      <td>feverish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0                  1                2                3   \\\n",
       "0    vegetablelike             smudge    vegetablelike        depravity   \n",
       "1            logic           seasoned  self-possession     shiny-strong   \n",
       "2          strikes  boulder-sprinkled        wednesday            logic   \n",
       "3  two-dimensional         refreshing  two-dimensional         outlined   \n",
       "4           ransom     vindictiveness        stockwell           ransom   \n",
       "5     pigmentation         compounded     unimaginable       squelching   \n",
       "6       squelching                lil             cool  police-sergeant   \n",
       "7        stockwell            mauvais       refreshing     pigmentation   \n",
       "8       dispirited          destroyer     pigmentation           haunch   \n",
       "9           oldest       unimaginable            domes           oldest   \n",
       "\n",
       "                4                5                6              7   \\\n",
       "0          strikes        stockwell        depravity      heel-rope   \n",
       "1           haunch  police-sergeant           haunch   pigmentation   \n",
       "2    sculptureless    vegetablelike  two-dimensional     refreshing   \n",
       "3           ransom     understand!i          poltoos    candelabrum   \n",
       "4              lil           oldest       squelching          logic   \n",
       "5    vegetablelike           haunch            shred            lil   \n",
       "6    honeycombings            domes           ransom  sculptureless   \n",
       "7           oldest        plumstead    vegetablelike  metal-colored   \n",
       "8       refreshing           swears  self-possession   unimaginable   \n",
       "9  police-sergeant        cyclopean         outlined          noisy   \n",
       "\n",
       "                8                9                10               11  \\\n",
       "0     pigmentation    honeycombings             moth  self-possession   \n",
       "1            domes  self-possession      gravitating  two-dimensional   \n",
       "2            logic              lil            domes         outlined   \n",
       "3           haunch           oldest   unastronomical            shred   \n",
       "4              lil  police-sergeant         attempts           haunch   \n",
       "5       squelching       squelching         drawling    vegetablelike   \n",
       "6  self-possession        stockwell       powerfully        stockwell   \n",
       "7  police-sergeant          strikes  police-sergeant       squelching   \n",
       "8           ransom    vegetablelike             bows    mine-sweepers   \n",
       "9           oldest    sculptureless           salted          poltoos   \n",
       "\n",
       "                12               13               14                  15  \\\n",
       "0     pigmentation         juncture  self-possession           pendulous   \n",
       "1        depravity      disarranged    sculptureless             snarled   \n",
       "2  self-possession          stretch  two-dimensional               films   \n",
       "3            shred  two-dimensional        stockwell               logic   \n",
       "4    vegetablelike          outfits           oldest              decade   \n",
       "5          strikes        hideously            logic  remarkable-looking   \n",
       "6         outlined    mine-sweepers         outlined                mars   \n",
       "7      candelabrum       nothwendig    vegetablelike        pigmentation   \n",
       "8  two-dimensional    blasphemously       dispirited           ancestors   \n",
       "9        stockwell           deaths           haunch             deposit   \n",
       "\n",
       "                 16               17           18               19  \n",
       "0              lush         indulged       haunch    half-carrying  \n",
       "1            flyers            domes  exclamation         cloudily  \n",
       "2             brook  two-dimensional       billow           doomed  \n",
       "3      consequences    honeycombings        dried           screws  \n",
       "4  official-looking     pigmentation      glimpse  two-dimensional  \n",
       "5     corroborative          weighty        jumpy          strikes  \n",
       "6           russian        guardedly   dispirited            nasal  \n",
       "7            panted  self-possession     gladness     pigmentation  \n",
       "8             wails       powerfully        petra            day's  \n",
       "9        broadcloth       refreshing      flounce         feverish  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topic = topics_df.T\n",
    "doc_topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc_topic_matrix_gensim = pre.make_doc_topic_matrix(model, doc2bow_list, doc2id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Doyle_AScandalinBohemia</th>\n",
       "      <th>Doyle_AStudyinScarlet</th>\n",
       "      <th>Doyle_TheHoundoftheBaskervilles</th>\n",
       "      <th>Doyle_TheSignoftheFour</th>\n",
       "      <th>Howard_GodsoftheNorth</th>\n",
       "      <th>Howard_SchadowsinZamboula</th>\n",
       "      <th>Howard_ShadowsintheMoonlight</th>\n",
       "      <th>Howard_TheDevilinIron</th>\n",
       "      <th>Kipling_TheEndofthePassage</th>\n",
       "      <th>Kipling_TheJungleBook</th>\n",
       "      <th>Kipling_ThyServantaDog</th>\n",
       "      <th>Lovecraft_AttheMountainofMadness</th>\n",
       "      <th>Lovecraft_TheShunnedHouse</th>\n",
       "      <th>Poe_EurekaAProsePoem</th>\n",
       "      <th>Poe_TheCaskofAmontillado</th>\n",
       "      <th>Poe_TheMasqueoftheRedDeath</th>\n",
       "      <th>Poe_ThePurloinedLetter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.110925</td>\n",
       "      <td>0.132284</td>\n",
       "      <td>0.117435</td>\n",
       "      <td>0.124426</td>\n",
       "      <td>0.119542</td>\n",
       "      <td>0.128699</td>\n",
       "      <td>0.126806</td>\n",
       "      <td>0.138573</td>\n",
       "      <td>0.131304</td>\n",
       "      <td>0.124935</td>\n",
       "      <td>0.146089</td>\n",
       "      <td>0.117728</td>\n",
       "      <td>0.117584</td>\n",
       "      <td>0.114796</td>\n",
       "      <td>0.095734</td>\n",
       "      <td>0.121242</td>\n",
       "      <td>0.119887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.197019</td>\n",
       "      <td>0.189266</td>\n",
       "      <td>0.191150</td>\n",
       "      <td>0.204241</td>\n",
       "      <td>0.183462</td>\n",
       "      <td>0.195170</td>\n",
       "      <td>0.201164</td>\n",
       "      <td>0.194774</td>\n",
       "      <td>0.182235</td>\n",
       "      <td>0.189734</td>\n",
       "      <td>0.179095</td>\n",
       "      <td>0.182794</td>\n",
       "      <td>0.163722</td>\n",
       "      <td>0.191503</td>\n",
       "      <td>0.187748</td>\n",
       "      <td>0.179353</td>\n",
       "      <td>0.179101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.354294</td>\n",
       "      <td>0.320902</td>\n",
       "      <td>0.339018</td>\n",
       "      <td>0.314830</td>\n",
       "      <td>0.307924</td>\n",
       "      <td>0.318302</td>\n",
       "      <td>0.292476</td>\n",
       "      <td>0.291261</td>\n",
       "      <td>0.290434</td>\n",
       "      <td>0.299794</td>\n",
       "      <td>0.293766</td>\n",
       "      <td>0.323006</td>\n",
       "      <td>0.313198</td>\n",
       "      <td>0.352680</td>\n",
       "      <td>0.361564</td>\n",
       "      <td>0.321201</td>\n",
       "      <td>0.347107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.121539</td>\n",
       "      <td>0.133120</td>\n",
       "      <td>0.127482</td>\n",
       "      <td>0.010170</td>\n",
       "      <td>0.010574</td>\n",
       "      <td>0.109703</td>\n",
       "      <td>0.124790</td>\n",
       "      <td>0.115877</td>\n",
       "      <td>0.150718</td>\n",
       "      <td>0.140823</td>\n",
       "      <td>0.146237</td>\n",
       "      <td>0.121906</td>\n",
       "      <td>0.126267</td>\n",
       "      <td>0.115992</td>\n",
       "      <td>0.014631</td>\n",
       "      <td>0.106262</td>\n",
       "      <td>0.011939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166780</td>\n",
       "      <td>0.181134</td>\n",
       "      <td>0.181988</td>\n",
       "      <td>0.132031</td>\n",
       "      <td>0.108497</td>\n",
       "      <td>0.198632</td>\n",
       "      <td>0.206703</td>\n",
       "      <td>0.218590</td>\n",
       "      <td>0.215900</td>\n",
       "      <td>0.208162</td>\n",
       "      <td>0.201091</td>\n",
       "      <td>0.214315</td>\n",
       "      <td>0.224919</td>\n",
       "      <td>0.189088</td>\n",
       "      <td>0.132407</td>\n",
       "      <td>0.228986</td>\n",
       "      <td>0.123847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.016186</td>\n",
       "      <td>0.016316</td>\n",
       "      <td>0.181297</td>\n",
       "      <td>0.220820</td>\n",
       "      <td>0.021233</td>\n",
       "      <td>0.018763</td>\n",
       "      <td>0.015898</td>\n",
       "      <td>0.012121</td>\n",
       "      <td>0.014641</td>\n",
       "      <td>0.010999</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>0.023572</td>\n",
       "      <td>0.013701</td>\n",
       "      <td>0.178526</td>\n",
       "      <td>0.013849</td>\n",
       "      <td>0.183857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.019614</td>\n",
       "      <td>0.013476</td>\n",
       "      <td>0.015307</td>\n",
       "      <td>0.014394</td>\n",
       "      <td>0.020318</td>\n",
       "      <td>0.014274</td>\n",
       "      <td>0.019114</td>\n",
       "      <td>0.014483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012983</td>\n",
       "      <td>0.014796</td>\n",
       "      <td>0.014861</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010766</td>\n",
       "      <td>0.012430</td>\n",
       "      <td>0.011781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Doyle_AScandalinBohemia  Doyle_AStudyinScarlet  \\\n",
       "0                 0.110925               0.132284   \n",
       "1                 0.197019               0.189266   \n",
       "2                 0.354294               0.320902   \n",
       "3                 0.121539               0.133120   \n",
       "4                 0.166780               0.181134   \n",
       "5                 0.020077               0.016186   \n",
       "6                 0.019614               0.013476   \n",
       "\n",
       "   Doyle_TheHoundoftheBaskervilles  Doyle_TheSignoftheFour  \\\n",
       "0                         0.117435                0.124426   \n",
       "1                         0.191150                0.204241   \n",
       "2                         0.339018                0.314830   \n",
       "3                         0.127482                0.010170   \n",
       "4                         0.181988                0.132031   \n",
       "5                         0.016316                0.181297   \n",
       "6                         0.015307                0.014394   \n",
       "\n",
       "   Howard_GodsoftheNorth  Howard_SchadowsinZamboula  \\\n",
       "0               0.119542                   0.128699   \n",
       "1               0.183462                   0.195170   \n",
       "2               0.307924                   0.318302   \n",
       "3               0.010574                   0.109703   \n",
       "4               0.108497                   0.198632   \n",
       "5               0.220820                   0.021233   \n",
       "6               0.020318                   0.014274   \n",
       "\n",
       "   Howard_ShadowsintheMoonlight  Howard_TheDevilinIron  \\\n",
       "0                      0.126806               0.138573   \n",
       "1                      0.201164               0.194774   \n",
       "2                      0.292476               0.291261   \n",
       "3                      0.124790               0.115877   \n",
       "4                      0.206703               0.218590   \n",
       "5                      0.018763               0.015898   \n",
       "6                      0.019114               0.014483   \n",
       "\n",
       "   Kipling_TheEndofthePassage  Kipling_TheJungleBook  Kipling_ThyServantaDog  \\\n",
       "0                    0.131304               0.124935                0.146089   \n",
       "1                    0.182235               0.189734                0.179095   \n",
       "2                    0.290434               0.299794                0.293766   \n",
       "3                    0.150718               0.140823                0.146237   \n",
       "4                    0.215900               0.208162                0.201091   \n",
       "5                    0.012121               0.014641                0.010999   \n",
       "6                    0.000000               0.012983                0.014796   \n",
       "\n",
       "   Lovecraft_AttheMountainofMadness  Lovecraft_TheShunnedHouse  \\\n",
       "0                          0.117728                   0.117584   \n",
       "1                          0.182794                   0.163722   \n",
       "2                          0.323006                   0.313198   \n",
       "3                          0.121906                   0.126267   \n",
       "4                          0.214315                   0.224919   \n",
       "5                          0.013632                   0.023572   \n",
       "6                          0.014861                   0.023000   \n",
       "\n",
       "   Poe_EurekaAProsePoem  Poe_TheCaskofAmontillado  Poe_TheMasqueoftheRedDeath  \\\n",
       "0              0.114796                  0.095734                    0.121242   \n",
       "1              0.191503                  0.187748                    0.179353   \n",
       "2              0.352680                  0.361564                    0.321201   \n",
       "3              0.115992                  0.014631                    0.106262   \n",
       "4              0.189088                  0.132407                    0.228986   \n",
       "5              0.013701                  0.178526                    0.013849   \n",
       "6              0.000000                  0.010766                    0.012430   \n",
       "\n",
       "   Poe_ThePurloinedLetter  \n",
       "0                0.119887  \n",
       "1                0.179101  \n",
       "2                0.347107  \n",
       "3                0.011939  \n",
       "4                0.123847  \n",
       "5                0.183857  \n",
       "6                0.011781  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topic_matrix_gensim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisierung "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lda_model = 'out_easy/corpus.lda'\\ncorpus = 'out_easy/corpus.mm'\\ndictionary = 'out_easy/corpus.dict'\\ndoc_labels = 'out_easy/corpus_doclabels.txt'\\ninteractive  = False\\n\\nvis = visual.Visualization(lda_model, corpus, dictionary, doc_labels, interactive)\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''lda_model = 'out_easy/corpus.lda'\n",
    "corpus = 'out_easy/corpus.mm'\n",
    "dictionary = 'out_easy/corpus.dict'\n",
    "doc_labels = 'out_easy/corpus_doclabels.txt'\n",
    "interactive  = False\n",
    "\n",
    "vis = visual.Visualization(lda_model, corpus, dictionary, doc_labels, interactive)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create a document-topic matrix (that is a pandas data frame actually)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "doc_topic = visual.create_doc_topic(mm, model, doc_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize document-topic matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'c:\\\\users\\\\philip\\\\appdata\\\\local\\\\programs\\\\python\\\\python35-32\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Md78y7gUzc58whgIFFkYc17pzw0SBeSmcrHX7H9fnlIFWoVmBJpZFUdTcS0jT6RNRcpOQ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\nHMNJkjScL/MY89NG2ilMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa89fb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "visual.doc_topic_heatmap(doc_topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize topic distribution in a single document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7e5GSGcClwHl11jc5IpYCSyW9WGhnHrBTE8e81v6YDlwsaSjwq4iYU7XOvUnv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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc0d94b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual.plot_doc_topics(doc_topic, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![success](http://cdn2.hubspot.net/hub/128506/file-446943132-jpg/images/computer_woman_success.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}